A novel sine and cosine algorithm for global optimization
Mostafa Meshkat, Mohsen Parhizgar · 2017
This study presents a new sine and cosine (S&C) optimization algorithm using a novel position update approach. In the proposed algorithm, the position update procedure for each search agent is determined by two coefficients, namely the exploration rate and the exploitation rate. These coefficients are updated in each run of the algorithm and provide an appropriate balance between the exploration and exploitation phases. The performances of the proposed algorithm and the sine cosine algorithm (SCA) were evaluated on a set of benchmark functions. The results indicate that in addition to a faster convergence speed, the S&C algorithm achieved the global best with a higher accuracy.